@article{Qiu2023, 
author = {Jun-hao Qiu and Zhi-jian Cheng and Guo-huai Lin and Hong-ru Ren and Ren-quan Lu},
title = {Prescribed Performance Control for a Class of Nonlinear Pure-feedback Systems with Actuator Faults},
year = {2023},
journal = {Journal of Guangdong University of Technology},
volume = {40},
number = {2},
pages = {55-63},
keywords = {adaptive neural networks control, actuator faults, full state constraints, prescribed performance, output feedback, dynamic surface control},
url = {https://www.sciopen.com/article/10.12052/gdutxb.210110},
doi = {10.12052/gdutxb.210110},
abstract = {In this paper, considering a class of nonlinear systems with full state constraints and actuator faults, an adaptive neural network output feedback fault-tolerant control algorithm with prescribed performance is proposed. A state observer is constructed to solve the unmeasurable states problem. Unknown nonlinear functions in the systems are approximated by radial basis function neural networks (RBF NNs) . By introducing the nonlinear mapping, the systems with state constraints are transformed into novel systems without state constraints. Moreover, a novel performance function is utilized to guarantee that the tracking error converges within a preset time. Meanwhile, the convergence speed can be adjusted through the parameter design. Finally, it is proved that the control algorithm ensures that all signals in the closed-loop systems are semi-globally uniformly ultimately bounded. The effectiveness of the algorithm is verified by a numerical simulation.}
}